Human Factors for Nursing: From In-Situ Testing to Mobile Usability Engineering
Bibliographic record
Abstract
The tutorial goal is to familiarize participants with human aspects of health informatics and human-centered approaches to the design, evaluation and deployment of both usable and safe healthcare information systems. The focus will be on demonstrating and teaching practical and low-cost methods for evaluating mobile applications in nursing. Basic background to testing methods will be provided, followed by live demonstration of the methods. Then the audience will break into small groups to explore the application of the methods to applications of interest (there will be a number of possible applications that will be available for applications in areas such as electronic health records and decision support, however, if the groups have applications of specific interest to them that will be possible). The challenges of conducting usability testing, and in particular mobile usability testing will be discussed along with practical solutions. The target audience includes practicing nurses and nurse researchers, nursing informatics specialists, nursing students, nursing managers and health informatics professionals interested in improving the usability and safety of healthcare applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".